CC BY-NC 4.0De Jesus, CindyLedda, Mark Kristian2026-09-012026-09-012021-03De Jesus, C. G., & Ledda, M. K. C. (2020). Intervention Support Program for Students at Risk of Dropping Out Using Fuzzy Logic-Based Prescriptive Analytics, 2021 IEEE 17th International Colloquium on Signal Processing & Its Applications (CSPA). Institute of Electrical and Electronics Engineers Inc. 144-149. doi: 10.1109/CSPA52141.2021.9377304.DOI:10.1109/CSPA52141.2021.9377304https://lakasa.dmmmsu.edu.ph/handle/123456789/2439Full textEducation is perceived to be an inevitable impact in building one’s nation and presumed to be a significant factor of one’s success. However, the issue with increasing school dropouts in secondary schools continue to persist worldwide despite this notion. This study aimed to design and develop an intervention support program for students at risk of dropping using prescriptive analytics for the Department of Education. It identified factors affecting students to drop such as family, individual, community and school related factors. Based from these factors, appropriate types of intervention programs were determined through focus group discussions with secondary school teachers and guidance counselors. A web-based intervention support program system was developed with the use of Fuzzy Logic-Based prescriptive analytics. First, the system predicts students at risk of dropping through the identified factors as inputs such as written work, performance task, quarterly exam, tardiness, absences, and results from the students’ guidance profiling. Second, based from the results of the prediction, the system’s fuzzy inference mechanism determines both the intervention applicability and effectivity in order to provide suitable intervention prescription as the system’s final output. The study found out that students who are at risk of dropping can be identified earlier with the correct inputs in the developed system and appropriate interventions vary from one student to another. Thus, the study is found to be useful in addressing the issue with increasing school dropouts by prescribing suitable intervention programs.enSOCIAL SCIENCES::Social sciences::EducationHigh school dropouts -- PreventionDropouts -- Prevention -- Data processingPrescriptive analyticsFuzzy logicEducational decision making -- Data processingEducation, Secondary -- Philippines -- Baguio CityEarly school leavers (Secondary Education)Educational tests and measurementsEvaluationAcademic predictive modelsArtificial intelligence (Fuzzy logic)High school dropouts -- Prevention -- Data processingDropouts -- Prevention -- Philippines -- BaguioEducational indicators -- Data processingFuzzy logic -- Educational applicationsEducational evaluation -- Data processingArticle